Multi–parametric prostate MRI (mpMRI) is a powerful tool to diagnose prostate cancer, though difficult to interpret even for experienced radiologists. A common radiological procedure is to compare a magnetic resonance image with similarly diagnosed cases. To assist the radiological image interpretation process, computerized Content–Based Image Retrieval systems (CBIRs) can therefore be employed to improve the reporting workflow and increase its accuracy. In this paper, we propose a new, supervised siamese deep learning architecture able to handle multi–modal and multi–view MR images with similar PIRADS score. An experimental comparison with well–established deep learning–based CBIRs (namely standard siamese networks and autoencoders) showed significantly improved performance with respect to both diagnostic (ROC–AUC), and information retrieval metrics (Precision–Recall, Discounted Cumulative Gain and Mean Average Precision). Finally, the new proposed multi–view siamese network is general in design, facilitating a broad use in diagnostic medical imaging retrieval.